In Equation REF , \(\gamma \in [0, 1]\)  is a discounting factor for rewards from future actions. For catering to the large intractable storage of tabular representation of the Q-function, deep neural networks come to the rescue to act as an approximator in the deep Q-Network (DQN) to represent the state-action value function [1]}. In RELEVAGAN, we use double DQN as the core technique for DRL-based attacks generation.
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